sensor network embedded intelligence
DESCRIPTION
Sensor Network Embedded Intelligence. A Al- Anbuky , H Sabat , M I Rawi & S Sivakumar SeNSe Research Centre http://SenSe.aut.ac.nz AUT University, Auckland. Presentation Overview. Info on the upcoming Co-Located ICT conferences 2010 SeNSe lab research overview - PowerPoint PPT PresentationTRANSCRIPT
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Sensor Network Embedded Intelligence
A Al-Anbuky, H Sabat, M I Rawi & S SivakumarSeNSe Research Centrehttp://SenSe.aut.ac.nz
AUT University, Auckland
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Presentation OverviewInfo on the upcoming Co-Located ICT
conferences 2010SeNSe lab research overviewHuman Comfort and passive house researchWildlife Sensor network and network
connectivity researchData stream mining research
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http://APCC2010.aut.ac.nz
2010 Co-located ICT conferences31 Oct – 3 Nov Auckland NZ
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http://APCC2010.aut.ac.nz
2010 Co-located ICT conferences31 Oct – 3 Nov Auckland NZ
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Empowering global connectivityToday we are confronted by global challenges such as climate change, resource consumption, environmental stress and population health. In responding to these challenges engineers are recognising the increasing importance of communications and connectivity. Sensor networks provide unprecedented volumes of information about our environments. Wireless and fixed communications networks facilitate the sharing of this information. Intelligence and cognition enable the efficient use and management of our resources. Meanwhile, humans and devices demand increasing communications connectivity and systems interoperability. Under the theme of "Empowering global connectivity", APCC 2010 provides a forum for researchers and engineers in the Asia-Pacific region to present and discuss topics related to advances in information and communication technologies, while encouraging collaboration and innovation that may help in saving the planet.
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Partnership & Fund Raising
Available opportunities varies from $2.5k to $20k
Sponsors privileges could include Seats for membership within organizing
committee (Key sponsors only)Free seats for conference Registration
(sponsorship dependent) Logos on CFP, Conference web site and
proceedingListed as sponsor within the proceedings
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The Venue
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The Venue
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SeNSe Lab -AUT
Wildlife Cognitive Sensor Network Mobile subjects localization Connectivity & opportunistic networks Wildfire hazard detection Hunters friendly fire avoidance Data stream mining & network energy efficiency
Object Centric Ambient Intelligence Human comfort & passive home ambient intelligence Thermal mapping & food property dynamic tracking pH sensor network & red meat tenderization
Vehicular Communication Train localization & railway signalling system
Microwave Sensing Timber property mapping
Distributed Signature Analysis Power System fault detection
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Passive House Sensor NetworksMohD Izani Rawi
SeNSe LabAUT University
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Overview
Passive House System OverviewArchitecture OverviewPassive House System ManagerThermal Comfort
Human Centric Thermal Comfort ConceptThermal Comfort OperationThermal Comfort SimulationThermal Comfort Result
Discussion & Further Work
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Passive House System Overview
Architecture Overview
Human Centric Activity – Automation, personalisation, adaptation
Going homeMobile Device (ID)
Sensors / Actuators(Location, appliances, environmental)
•Going home•Mobile Device Notify Home•Personalise home environment•Learn occupant behaviour•Adaptation & personalisation
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Passive House System Overview
Passive House System Manager
Human Comfort
ThermalComfort
VisualComfort
Indoor Air
Comfort
Acoustical
Comfort
SpatialComfort
PMV Light AQ Noise
OccupantPreferenc
es
ThermalVisualAir
EnergyUsage
PH Manager
ActuatorControl
Heating / CoolingWindow PositionShading PositionIlluminance Level
Heating / CoolingHot WaterAppliancesVentilation
Ta, MRT, RH, VelClo, Met
Illuminance LevelShading Level
CO2 Concentration Sound Level
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Passive House System OverviewThermal Comfort
PMV Value Meaning+3 Hot+2 Warm+1 Slight Warm0 Neutral-1 Slight Cool-2 Cool-3 Cold
M: metabolismW: external work, equal to zero for most activityIcl: thermal resistance of clothingfcl: ratio of body’s surface area when fully clothed to body’s surface area when nudeta: air temperaturetr: mean radiant temperatureVa: air velocityPa: partial water vapour pressurehc: convectional heat transfer coefficienttcl: surface temperature of clothing
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Human Centric Thermal Comfort Concept
Thermal Comfort OperationSingle Node PMV CalculationsTested on Sun SPOT wireless platformSeNSe lab air temperature & PMV
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Human Centric Thermal Comfort ConceptThermal Comfort Simulation
PMV of a Given Living SpaceInverse Distance Weighted (IDW) interpolation
technique
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Human Centric Thermal Comfort ConceptThermal Comfort Results
Thermal Comfort Parameters
Nodes
N1 N2 N3 N4 N0
DBT 24 23 21
22 22.50
MRT 27 25 20
21 23.41
RH 50 57 60
54 55.94
Vel 0.1 0.1 0.2
0.2 0.14
Met - - - - 1
Clo - - - - 1
PMV at N0 = -0.16
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Energy Efficient Network Connectivity: Wildlife and Sensor Network
Sivakumar SivaramakrishnanSeNSe Lab
AUT University
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Connectivity Issues in Wildlife Monitoring
Short Range Nodes
Network is Adhoc
Network Holes (region of no connectivity)
Movement results in Temporary Connectivity
Node Discovery
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Varying Node DensityAnimals have different habitatThis determines the grouping of the nodes
Varying node Density Depending on Animal Habitat
Due to connectivity holes data transmission is opportunistic.
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B
• Opportunistic Networking• Data Hand-off Mechanism• Adaptive Node Discovery• Doppler Shift to Detect
Direction
Hand-Off under Random motion of the animal
Energy Dissipation for Connectivity with and without Hand-off
Adaptive Opportunistic Connectivity
A
DFC
E
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Due to Predictive Sampling: Fig. shows adaptive sampling saves on energy as the number of unsuccessful searches are less
Preliminary Results
Due to Hand-off: Fig. shows the energy consumption due to Hand-off scheme is less than without hand-off
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Distributed Data Stream Mining in WSN Environment:
Efficient Fuzzy based Approach
Hakilo SabitSeNSe Lab
AUT University
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Sensors data streamsA data stream can be roughly
thought as an ordered sequence of items, where the input arrives more or less continuously as time progresses.
Examples of data streams include computer network traffic, phone conversation, Web searches, Sensor data and etc.
Data streams are characterised by continuous flow of data with infinite length.
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Sensors deployed for monitoring application (ex. traffic flow monitoring, environmental monitoring, patient health monitoring) produce data with such (data stream) characteristics.
Data steams generate large quantity of real-time/near real-time data (structured records).
The stream processing has to be done in real-time or near real-time and in bounded storage.
Data stream processing
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WSN stream miningWSN are know for their limited resources (storage, processing
and energy).High resolution sensor data streams contain useful
information excellent environment for data mining
Fuzzy logic based distributed stream clustering algorithm (SUBFCM)
designed and optimised for WSN environment
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The SUBFCM algorithm SUBFCM compute local clusters at
designated GH nodes and only transmit the local representatives- Reduced data bits to transmit means energy saving, besides bandwidth efficiency.
Based on single scan of data items to extract the representative patterns & no intermediate data stored - memory scalable.
• SUBFCM compute the complete cluster at a central location based on the local representatives•Stream modelling results will generate a control signal for the local nodes to adjust their parameters
Internet
Fire Department
Local Industry
Residents Data processing
centre
Sink
Sensor
Group head
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0 20 40 60 80 100 1200
0.2
0.4
0.6
0.8
1
1.2
1.4x 10
-3
Total Distance [m]
Tota
l E
nerg
y [
Joule
s]
fcm, multi-hop fcm and subfcm algorithms
subfcm-->
-->fcm
multi-hop-->->q=70
24 25 26 27 28 29 3055
60
65
70
75
80
85
90
Temperature [oC]
Rela
tive H
um
idity [
%]
hot spot 1
hot spot 2
hot spot 3
0
5
10
15
20
25
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Erro
r
No. of runs
Temp Erorr Average Temp Error
RH Erorr Average RH Error
0
2
4
6
8
10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Erro
r
No. of runs
Temp Error Average Temp Error
RH Error Average RH Error
Energy consumption
Data reduction
Cluster accuracy vs central algorithm
Cluster accuracy vs fcm algorithm
Preliminary Results